Operation and maintenance decision-making using prognostic information

Practicable life extension of engineering systems would be a remarkable application of prognostics. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. This research investigates the use of prognostic data to mobilize the potential residual life. In this respect, a multi-objective optimization model is presented for a typical power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. The results of the optimization models quantitatively indicated that maximizing the service life of bearings requires lower shaft speed and longer maintenance time.